Tag: Data quality

A 2025 LinkedIn article highlighted that poor data quality costs companies millions annually, underscoring the critical need for a reliable Data Quality Index (DQI). The DQI is a metric that rates the overall health of an organization's data, empowering data professionals to measure trustworthiness, enhance decision-making, and improve operational efficiency. Calculating this index involves defining key dimensions, setting rules, and aggregating individual scores into a composite metric.

According to Gartner, businesses lose an average of $12.9 million annually due to poor data quality, a metric that underscores the financial impact of inaccurate values. In science, business, and everyday life, an accurate value is one that correctly reflects the true, real-world state of what is being measured or recorded. It is a foundational component of reliable data and sound decision-making, distinguishing itself from the concept of precision.

According to the World Health Organization (WHO), the Z-score system is the globally recommended method for interpreting anthropometric data, making Z scores useful for nutrition surveillance. This statistical measure provides a clear, standardized way to compare individual or population-level nutritional status against a healthy reference population.